{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_confusion_matrix method used by a scikit-learn classifier\n",
    "\n",
    "In this example, we'll be plotting a confusion matrix to describe the classification performance of a `RandomForestClassifier` using the digits dataset from scikit-learn. Here, we'll be using the `scikitplot.metrics.plot_confusion_matrix` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_digits as load_data\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (14, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "X, y = load_data(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the RandomForestClassifier\n",
    "classifier = RandomForestClassifier()\n",
    "\n",
    "# Perform predictions\n",
    "predictions = cross_val_predict(classifier, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7UBcM6KNQKKShlVzbz+vdVwtz56tTdjulpqbqoUnha/vWrYUaddUIFYVCKioq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16567aed6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot!\n",
    "plot = skplt.metrics.plot_confusion_matrix(y, predictions, normalize=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
